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What is MLOps?
MLOps (Machine Learning Operations) is the discipline that applies DevOps practices to machine learning projects. It encompasses processes, tools, and culture to reliably develop, deploy, and operate ML models.
Key areas: data management (versioning, quality, feature store), model development (experiments, tracking, reproducibility), deployment (CI/CD, A/B testing, rollbacks), monitoring (performance, drift, fairness), retraining (automated updates to new data).
Difference from DevOps: In addition to code, MLOps must also version, test, and deploy data and models. Model quality can degrade during operation (drift)—retraining and monitoring are key considerations. Additionally: fairness, explainability, and compliance.
For small and medium-sized businesses, MLOps represents the leap from a makeshift solution to standard operations. Without MLOps, every ML project remains a one-off solution. With MLOps, AI becomes a maintainable product—and the investment pays off even after the first model.
Best Practices for MLOps
These six principles have proven effective:
- Start small: Don’t try to do everything at once—increase maturity level by level.
- Version everything: code, data, models, configuration—ensure reproducibility.
- Test like software: Unit tests, integration tests, model tests—all automated.
- Monitor from the start: Don’t add monitoring later—monitor models and operations.
- Work cross-functionally: Data scientists, engineers, ops, and business users working together.
- Iterative improvement: Start with the basics, then automation, then advanced features.
Common Mistakes in MLOps
We often see these pitfalls:
- Too Many Tools Too Soon: Introducing all the tools at once — overwhelming the team.
- Data Science in a Silo: MLOps is just an IT thing—data scientists work in isolation.
- Notebooks in Production: Using Jupyter in production—not maintainable, not scalable.
- No feature store: Features are calculated differently in training and production—model quality suffers.
- Lack of monitoring: The model is running, but no one notices drift—until customers complain.
MLOps vs. DevOps vs. AIOps
A comparison of three related disciplines:
- DevOps: For traditional software — continuous integration, delivery, and deployment.
- MLOps: Extends DevOps to include data, models, drift, retraining, and fairness.
- AIOps: AI for IT operations — anomaly detection, root cause analysis, auto-remediation.
Building MLOps with prodot
In a free initial consultation, we’ll assess your MLOps maturity level and outline a roadmap for implementation—a pragmatic approach based on the maturity model.
As an AI partner for small and medium-sized businesses, we build practical MLOps setups—from the first pipeline to a fully automated platform.
What We Offer
- AI Consulting — MLOps Concept and Rollout.
- AI Pipeline in the Glossary — the core MLOps building block.
- AI Monitoring in the Glossary — keeping a constant eye on models.
- AIOps in the Glossary — MLOps’ neighbor on the IT side.
